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From the brand
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Machine Learning, AI & more
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Machine Learning
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Artificial Intelligence
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Deep Learning
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Language Processing (NLP, LLM)
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Sharing the knowledge of experts
O'Reilly's mission is to change the world by sharing the knowledge of innovators. For over 40 years, we've inspired companies and individuals to do new things (and do them better) by providing the skills and understanding that are necessary for success.
Our customers are hungry to build the innovations that propel the world forward. And we help them do just that.
From the Publisher
Prerequisites
This book assumes that you have some Python programming experience and that you are familiar with Python’s main scientific libraries, in particular NumPy, Pandas, and Matplotlib.
Also, if you care about what’s under the hood, you should have a reasonable understanding of college-level math as well (calculus, linear algebra, probabilities, and statistics).
About this Book
Machine Learning in Your Projects
So, naturally you are excited about Machine Learning and would love to join the party! Perhaps you'd like to give your homemade robot a brain of its own? Make it recognize faces? Or learn to walk around? Or maybe your company has tons of data (user logs, financial data, production data, machine sensor data, hotline stats, HR reports, etc.), and more than likely you could unearth some hidden gems if you just knew where to look. With Machine Learning, you can accomplish the following & much more:
- Segment customers and find the best marketing strategy for each group.
- Recommend products for each client based on what similar clients bought.
- Detect which transactions are likely to be fraudulent.
- Forecast next year’s revenue.
Objective and Approach
This book assumes that you know close to nothing about Machine Learning. Its goal is to give you the concepts, tools, and intuition you need to implement programs capable of learning from data.
We will cover a large number of techniques, from the simplest and most commonly used (such as Linear Regression) to some of the Deep Learning techniques that regularly win competitions. For this, we will be using production-ready Python frameworks:
- Scikit-Learn is very easy to use, yet it implements many Machine Learning algorithms efficiently, so it makes for a great entry point to learning Machine Learning.
- TensorFlow is a more complex library for distributed numerical computation. It makes it possible to train and run very large neural networks efficiently by distributing the computations across potentially hundreds of multi-GPU (graphics processing unit) servers. TensorFlow (TF) was created at Google and supports many of its large-scale Machine Learning applications.
- Keras is a high-level Deep Learning API that makes it very simple to train and run neural networks. Keras comes bundled with TensorFlow, and it relies on TensorFlow for all the intensive computations.
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
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Hands-On Machine Learning with Scikit-Learn and PyTorch
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| Libraries covered | Scikit-Learn, Keras, and TensorFlow | Scikit-Learn and PyTorch |
About the author
